Teaching Alexa to Follow Conversations

n order to engage customers in longer, more productive conversations, Alexa needs to solve the problem of reference resolution. If Alexa says, “‘Believer’ is by Imagine Dragons”, for instance, and the customer replies, “Play their latest album”, Alexa should be able to deduce that “their” refers to Imagine Dragons.

In the past, we’ve addressed the reference resolution problem by teaching a machine learning system to map correspondences between the variables used by different Alexa services. Alexa’s knowledge base, for instance, might store information about a song’s performer in a variable called [audio act], while the Alexa music service might store the same information in a variable called ArtistName. Learning those mappings requires lots of application-specific data, annotated with variable names.

This week, at the annual meeting of the North American chapter of the Association for Computational Linguistics, we presented a new approach to this problem that, in experiments, delivered much stronger results.

We show that this approach will scale better than previous approaches, and to encourage other researchers to pursue it, we’ve publicly released one of two data sets we used to demonstrate our system, which is based on an existing public data set.

Our new approach is to rewrite customer queries in natural language, substituting entity names and other identifying data for ambiguous references. When a customer says, “Play their latest album”, our system should rewrite the query as “Play Imagine Dragons’ latest album”.

Rather than trying to map variables onto each other across services ([audio act] = ArtistName), the new system rewrites queries in natural language (“their” = “Imagine Dragons’”). For each word of an input sequence, the contextual query rewrite engine adds a word to an output sequence, according to probabilities (blue bars) computed by a neural network.

This approach has several advantages. First, because our rewrite engine learns general principles of reference, it doesn't depend on any application-specific information, so it doesn't require retraining when we expand Alexa’s capabilities. Second, it frees backend code from worrying about referring expressions. Individual Alexa services can always assume that they have received a fully specified utterance. Finally, training data can be annotated by any competent language speaker, without specialized knowledge of Alexa’s internal nomenclature.

In addition to the data set that we have released publicly, we also tested our system on a larger in-house data set. We evaluated performance using F1 score, which measures both false-positive and false-negative rates. On the in-house data set, when a term in the current utterance referred to a term in the most recent system response, our new approach improved the F1 score by 22%. When a term in the current utterance referred to a term in the previous user utterance, the F1 score improved by 25%.Like our earlier variable-mapping system, our new system is a neural network. When Alexa’s NLU systems receive an utterance, they determine its intent, or the action that the customer wants performed, and they assign individual words to slots, which are the variables such as [audio act], [track work], ArtistName, or SongName. Slot values are used to identify the specific data items that customers want retrieved.

The input to our neural network includes the words of the current customer utterance; the words of several prior rounds of dialogue; the intent classification of each turn of dialogue; and, for all words, the slot tags provided by the NLU system.Wherever possible, however, our system replaces individual words with generic classifiers, such as ENTITYU1, for the first entity named by the user, or ENTITYS3, for the third entity named by the system. These generic classifiers do not replace the slot tags; they complement them.This approach allows the system to generalize much more effectively during training. It prevents the network from “overfitting”, or paying undue attention to particular characteristics of training examples, such as the individual words of song titles. Instead, it focuses the network's attention on the syntactic and semantic roles that words are playing.

Our network is a pointer network, which is a variation of the type of sequence-to-sequence (seq2seq) network commonly used in natural-language-generation tasks, such as machine translation. A seq2seq network processes an input sequence — such as string of words — in order and generates an output — such as the equivalent sentence in another language — one item at a time. A pointer network is a seq2seq network whose output is a sequence of references (or pointers) to the words of the input sequence.

With each new round of dialogue, we encode the complete dialogue using a long short-term memory, a type of neural network that remembers the data it’s seen recently and modifies its outputs accordingly. Once the dialogue encoding is up to date, the system begins to rewrite the latest customer utterance, one word at a time. For each word, it decides whether to generate a new word from a list of commonly occurring words or to copy a word from the dialogue history.

In addition to our in-house data set, we also used a data set developed at Stanford University, along with crowd-sourced rewrites of dialogues from that data set, which we released publicly. Each dialogue was rewritten five times by annotators recruited through Mechanical Turk, who were asked to replace referring terms with their referents. Annotations that received majority votes were incorporated into the new data set.

In exchange for the use of their data set, Stanford has asked that we cite the following paper, which we are happy to do:

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Do you want to join Alexa AI -- the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world?If your answers to these questions are “yes”, then come join the Alexa Artificial Intelligence team. We are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.As an Applied Science Manager you will lead the science efforts to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. You will also:· Build a strong and coherent team with particular focus on automated ranking and arbitration, sciences, and innovation.· Serve as a technical lead on demanding and cross-team projects, and effectively collaborating with multiple cross-organizational teams· Apply technical influence on partner teams, increasing their productivity by sharing your deep knowledge.

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Amazon has built a reputation for excellence with recent examples of being named the #1 most trusted company for customers. The Selling Partner Abuse team's mission is to protect the trust of our stores for customers and selling partners. We see ourselves as the steward of customer trust and Amazon brand.We are seeking a Machine Learning Science Manager who will lead a team of Applied Scientists, Research Scientists and Data scientists to research and develop innovative machine learning and science solutions to prevent the bad activities in the Amazon stores.Your responsibilities will include:· Build and develop the core science team for Selling Partner Abuse· Create new projects to drive significant business impact and research advancement· Create project milestone and manage multiple projects end-to-end while quickly adapt to changing priorities and generate innovative solutions in an extremely fast-paced environment· Coach the team and continuously raise the bar on highest standard· Build a strong partnership across different business, engineering and science stakeholders· Manage different Machine Learning projects that cover different science areas such as Ensemble Tree learning models, Clustering, Anomaly Detection, Graph models, NLP models, Semi-supervised Learning models and Reinforcement Learning

Amazon delights millions of customers around the world. Meet the behind the scenes team that enables our Human Resource and Operations Leaders to make informed decisions. The Amazon PeopleInsight team builds reporting and analytics tools for our teams that fulfill customer promise every day. Whether it is Fulfillment Center team that delivers your Prime order in two days, our Amazon Locker team that lets you pick up your package anytime that is convenient for you, our Prime Now team getting you lunch in under an hour, or one of many more, the PeopleInsight group is there providing people metrics along the employee lifecycle for our global operations businesses. The PeopleInsight team is a collaborative group of Business Analysts, Business Intelligence Engineers, Data Engineers, Data Scientists, Product Managers, and Program Managers dedicated to empowering leaders and enabling action through data and science. We deliver workforce, associate experience, and leadership insights so Amazon leaders can focus their efforts in ways that will engage, retain and grow their associates.We are now recruiting for an exceptional Data Scientist, Worldwide OperationsThe ideal candidate will be:· A Well-Rounded Athlete –Like a true athlete, you understand that we succeed or fail as a team. You are always ready to step up beyond your core responsibilities and go the extra mile for the project and your team. You nimbly overcome barriers to deliver the best products more quickly than expected.· A Perpetual Student – You seek knowledge and insight. You challenge yourself to turn moments into master’s classes. Whether closing a gap, developing a new skill, or staying ahead of your industry, you revel in the joy of learning and growing.· A Skilled Communicator – You excel when interacting with business and technical partners whether you are chatting, sending a written message, or conducting a presentation.· A Trusted Advisor – You work closely with stakeholders to define key business needs and deliver on commitments. You enable effective decision making by retrieving and aggregating data from multiple sources and compiling it into a digestible and actionable format.· An Inventor at Heart – You innovate on behalf of your customer by proactively implementing improvements, enhancements, and customizations. Your customers marvel at your creative solutions to challenges they had not yet identified.· A Fearless Explorer – You are drawn to take on the hardest problems, navigate ambiguity, and battle skepticism. You never settle, even in the face of overwhelming obstacles.Roles and ResponsibilitiesSuccess in this role will include influencing within your team and mentoring peers. The problems you will consider will be difficult to solve and often require a range of data science methodologies combined with subject matter expertise. You will need to be capable of gathering and using complex data set across domains. You will deliver artifacts on medium size projects, define the methodology, and own the analysis. Your findings will affect important business decisions. Solutions are testable and reproducible. You will create documents and share findings in line with scientific best practices for both technical and nontechnical audiences.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.

The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata or R is necessary, and experience with SQL, UNIX, and Sawtooth would be a plus.These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement.Roughly 50% of research assistants from previous cohorts have converted to full time data science or economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, A9 Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search team works to maximize the quality and trustworthiness of the search experience for visitors to Amazon websites worldwide.Our mission is to provide customers' trust and confidence in Amazon Search shopping experience. We identify problems that are customer trust busters at Amazon, deliver scalable and responsive solutions to these issues, and build experiences that gain customer trust using advanced machine learning methods. We carefully monitor the trustworthiness of the search results and dive deep when we see an unusual pattern. Most of the models used by our team is semi-supervised or unsupervised using small amount of labeled data.In this role you will leverage your strong statistical background to help build the next generation of our machine learning methods to discover untrustworthy search engagements, unsual patterns, and estimate a probability of risk for each item. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models, graph algorithms, and information retrieval algorithms at scale. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses.If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you.Major responsibilities· · Use machine learning and analytical techniques to create scalable solutions for business problems· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes· · Design, development, evaluate and deploy innovative and highly scalable models for predictive learning· · Research and implement novel machine learning and statistical approaches· · Work closely with software engineering teams to drive real-time model implementations and new feature creations· · Work closely with business owners and operations staff to optimize various business operations· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation· · Mentor other scientists and engineers in the use of ML techniques

Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.The Moderation and Relevance System (MARS) team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages.In this role, you will build and develop ML models to address content intelligence problems, build advanced algorithms in detecting and generating content. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. You will propose hypotheses, validate these offline and run A/B tests to validate them online. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.